Identification of shared genes and pathways: a comparative study of multiple sclerosis susceptibility, severity and response to interferon beta treatment.

Mahurkar S, Moldovan M, Suppiah V, O'Doherty C - PLoS ONE (2013)

Bottom Line:
Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes.By applying the systems biology based approach, additional significant information can be extracted from GWAS.Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.

Affiliation: School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, Australia.

ABSTRACTRecent genome-wide association studies (GWAS) have successfully identified several gene loci associated with multiple sclerosis (MS) susceptibility, severity or interferon-beta (IFN-ß) response. However, due to the nature of these studies, the functional relevance of these loci is not yet fully understood. We have utilized a systems biology based approach to explore the genetic interactomes of these MS related traits. We hypothesised that genes and pathways associated with the 3 MS related phenotypes might interact collectively to influence the heterogeneity and unpredictable clinical outcomes observed. Individual genetic interactomes for each trait were constructed and compared, followed by prioritization of common interactors based on their frequencies. Pathway enrichment analyses were performed to highlight shared functional pathways. Biologically relevant genes ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A were identified as common to all 3 MS phenotypes. We observed that the highest number of first degree interactors were shared between MS susceptibility and MS severity (p = 1.34×10(-79)) with UBC as the most prominent first degree interactor for this phenotype pair from the prioritisation analysis. As expected, pairwise comparisons showed that MS susceptibility and severity interactomes shared the highest number of pathways. Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes. Finally, FYN was the most common first degree interactor in the MS drugs-gene network. By applying the systems biology based approach, additional significant information can be extracted from GWAS. Results of our interactome analyses are complementary to what is already known in the literature and also highlight some novel interactions which await further experimental validation. Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.

Mentions:
In order to determine the significance of these results, a hypergeometric test was applied to each of the paired categories (Table S4). The susceptibility - severity pair, which had 160 common first degree interacting genes, showed the strongest evidence of statistical significance (p-value = 1.34×10−79), followed by the susceptibility - IFN-ß response pair (p-value = 6.89×10−24), and then the severity - IFN-ß response pair (p-value = 1.74×10−6). Interactor scores were calculated with the intention of eliminating a bias due to varying numbers of studies, generating varying number of interactors and interactions for each gene (Figure 2).

Mentions:
In order to determine the significance of these results, a hypergeometric test was applied to each of the paired categories (Table S4). The susceptibility - severity pair, which had 160 common first degree interacting genes, showed the strongest evidence of statistical significance (p-value = 1.34×10−79), followed by the susceptibility - IFN-ß response pair (p-value = 6.89×10−24), and then the severity - IFN-ß response pair (p-value = 1.74×10−6). Interactor scores were calculated with the intention of eliminating a bias due to varying numbers of studies, generating varying number of interactors and interactions for each gene (Figure 2).

Bottom Line:
Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes.By applying the systems biology based approach, additional significant information can be extracted from GWAS.Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.

Affiliation:
School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, Australia.

ABSTRACTRecent genome-wide association studies (GWAS) have successfully identified several gene loci associated with multiple sclerosis (MS) susceptibility, severity or interferon-beta (IFN-ß) response. However, due to the nature of these studies, the functional relevance of these loci is not yet fully understood. We have utilized a systems biology based approach to explore the genetic interactomes of these MS related traits. We hypothesised that genes and pathways associated with the 3 MS related phenotypes might interact collectively to influence the heterogeneity and unpredictable clinical outcomes observed. Individual genetic interactomes for each trait were constructed and compared, followed by prioritization of common interactors based on their frequencies. Pathway enrichment analyses were performed to highlight shared functional pathways. Biologically relevant genes ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A were identified as common to all 3 MS phenotypes. We observed that the highest number of first degree interactors were shared between MS susceptibility and MS severity (p = 1.34×10(-79)) with UBC as the most prominent first degree interactor for this phenotype pair from the prioritisation analysis. As expected, pairwise comparisons showed that MS susceptibility and severity interactomes shared the highest number of pathways. Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes. Finally, FYN was the most common first degree interactor in the MS drugs-gene network. By applying the systems biology based approach, additional significant information can be extracted from GWAS. Results of our interactome analyses are complementary to what is already known in the literature and also highlight some novel interactions which await further experimental validation. Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.